def show_results(p):
img = cv2.imread(p)
img = cv2.cvtColor(img, cv2.IMREAD_GRAYSCALE)
faces = face_model.detectMultiScale(img,scaleFactor=1.1, minNeighbors=4)
label = [0 for i in range(len(faces))]
new_img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) #colored output image
counter_masks = {
'has_mask': 0,
'no_mask': 0
}
for i in range(len(faces)):
(x,y,w,h) = faces[i]
crop = new_img[y:y+h,x:x+w]
crop = cv2.resize(crop,(128,128))
crop = np.reshape(crop,[1,128,128,3])/255.0
mask_result = model.predict(crop)
has_mask = mask_result.argmax()
if has_mask==0:
counter_masks['has_mask'] += 1
else:
counter_masks['no_mask'] += 1
cv2.putText(new_img,mask_label[has_mask],(x, y-10),cv2.FONT_HERSHEY_SIMPLEX,0.5,rect_label[has_mask],2)
cv2.rectangle(new_img,(x,y),(x+w,y+h),rect_label[has_mask],1)
plt.figure(figsize=(10,10))
annot1 = f"People with MASKS: {counter_masks['has_mask']};"
annot2 = f"People with NO MASKS: {counter_masks['no_mask']}."
cv2.putText(new_img,text=annot1,org=(10, 10),fontFace=cv2.FONT_HERSHEY_TRIPLEX,fontScale=0.5,color=(255, 255, 255),thickness=1)
cv2.putText(new_img,text=annot2,org=(10, 30),fontFace=cv2.FONT_HERSHEY_TRIPLEX,fontScale=0.5,color=(255, 255, 255),thickness=1)
plt.imshow(new_img)
